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1. Promoting Multidimensional Equity through Collaborative Routing using Incentive Mechanisms

   Wang, C; Peeta, S (January 2024, TRB)

   Penalty-based strategies, such as congestion pricing, have been employed to improve traffic network efficiency, but they face criticism for their negative impact on users and equity concerns. Collaborative routing, which allows users to negotiate route choices, offers a solution that considers individual heterogeneity. Personalized incentives can encourage such collaboration and are more politically acceptable than penalties. This study proposes a collaborative routing strategy that uses personalized incentives to guide users towards desired traffic states while promoting multidimensional equity. Three equity dimensions are considered: accessibility equity (equal access to jobs, services, and education), inclusion equity (route suggestions and incentives that do not favor specific users), and utility equity (envy-free solutions where no user feels others have more valuable incentives). The strategy prioritizes equitable access to societal services and activities, ensuring accessibility equity in routing solutions. Inclusion equity is maintained through non-negative incentives that consider user heterogeneity without excluding anyone. An envy-free compensation mechanism achieves utility equity by eliminating envy over incentive-route bundles. A constrained traffic assignment (CTA) formulation and consensus optimization variant are then devised to break down the centralized problem into smaller, manageable parts and a decentralized algorithm is developed for scalability in large transportation networks and user populations. Numerical studies investigate the model's enhancement of equity dimensions and the impact of hyperparameters on system objective tradeoffs and demonstrate the algorithm convergence. 

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2. Personalized Freight Route Recommendations with System Optimality Considerations: A Utility Learning Approach

   Aristotelis-Angelos Papadopoulos; Ioannis Kordonis; Maged Dessouky; Petros Ioannou (January 2023, IEEE transactions on intelligent transportation systems)

   Traffic congestion has a negative economic and environmental impact. Traffic conditions become even worse in areas with high volume of trucks. In this paper, we propose a coordinated pricing-and-routing scheme for truck drivers to efficiently route trucks into the network and improve the overall traffic conditions. A basic characteristic of our approach is the fact that we provide personalized routing instructions based on drivers’ individual routing preferences. In contrast with previous works that provide personalized routing suggestions, our approach optimizes over a total system-wide cost through a combined pricing-and-routing scheme that satisfies the budget balance on average property and ensures that every truck driver has an incentive to participate in the proposed mechanism by guaranteeing that the expected total utility of a truck driver (including payments) in case he/she decides to participate in the mechanism, is greater than or equal to his/her expected utility in case he/she does not participate. Since estimating a utility function for each individual truck driver is computationally intensive, we first divide the truck drivers into disjoint clusters based on their responses to a small number of binary route choice questions and we subsequently propose to use a learning scheme based on the Maximum Likelihood Estimation (MLE) principle that allows us to learn the parameters of the utility function that describes each cluster. The estimated utilities are then used to calculate a pricing-and-routing scheme with the aforementioned characteristics. Simulation results in the Sioux Falls network demonstrate the efficiency of the proposed pricing-and-routing scheme. 

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3. EFFICIENT BLOCKCHAIN AUTHENTICATION SCHEME FOR VANETS

   Wagner, Matthew; McMillin, Bruce (March 2020, Critical Infrastructure Protection XIV.)

   As the use of autonomous vehicles increase, the transportation infrastructure as a whole becomes more susceptible to cyber-attacks due to the increase of components that can communicate with one another and the Internet. It has been shown that autonomous vehicles benefit greatly from cooperating to perform many cost and life-saving applications such as tailgating, advanced collision warning, and even traffic routing. To secure the transportation infrastructure against this increasing risk, this paper presents an efficient blockchain scheme for vehicular ad-hoc networks of autonomous vehicles. In the proposed scheme, every vehicle maintains blocks generated by its platoon which contain transactions that evaluate the actions of every vehicle. Thus, vehicles will possess different blocks and thus different blockchains as they join and leave platoons. No central blockchain is maintained. These blocks are used as a token by the vehicle to gain access to future platoons. The proposed scheme uses the Schnorr digital signature scheme to create a secure signature and reach consensus within the platoon. It is proven to be secure under the given assumptions. 

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4. A Decentralized Approach For Real Time Anomaly Detection In Transportation Networks

   https://doi.org/10.1109/SMARTCOMP.2019.00063  

   Wilbur, Michael; Dubey, Abhishek; Bruno, Leao; Bhattacharjee, Shameek. (June 2019, 2019 IEEE International Conference on Smart Computing (SMARTCOMP))

   Internet of Things (IoT), edge/fog computing, and the cloud are fueling rapid development in smart connected cities. Given the increasing rate of urbanization, the advancement of these technologies is a critical component of mitigating demand on already constrained transportation resources. Smart transportation systems are most effectively implemented as a decentralized network, in which traffic sensors send data to small low-powered devices called Roadside Units (RSUs). These RSUs host various computation and networking services. Data driven applications such as optimal routing require precise real-time data, however, data-driven approaches are susceptible to data integrity attacks. Therefore we propose a multi-tiered anomaly detection framework which utilizes spare processing capabilities of the distributed RSU network in combination with the cloud for fast, real-time detection. In this paper we present a novel real time anomaly detection framework. Additionally, we focus on implementation of our framework in smart-city transportation systems by providing a constrained clustering algorithm for RSU placement throughout the network. Extensive experimental validation using traffic data from Nashville, TN demonstrates that the proposed methods significantly reduce computation requirements while maintaining similar performance to current state of the art anomaly detection methods. 

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5. TollsOnly Please – Homomorphic Encryption for Toll Transponder Privacy in Internet of Vehicles

   https://doi.org/10.1109/JIOT.2021.3056240  

   Karim, Hassan; Rawat, Danda B. (February 2021, IEEE Internet of Things Journal)

   null (Ed.)

   Cities have circumvented privacy norms and deployed sensors to track vehicles via toll transponders (like E-Zpass tags). The ethical problems regarding these practices have been highlighted by various privacy advocacy groups. The industry however, has yet to implement a standard privacy protection regime to protect users’ data. Further, existing risk management models do not adequately address user-controlled data sharing requirements. In this paper, we consider the challenges of protecting private data in the Internet of Vehicles (IoV) and mobile edge networks. Specifically, we present a privacy risk reduction model for electronic toll transponder data. We seek to preserve driver privacy while contributing to intelligent transportation infrastructure congestion automation schemes. We thus propose TollsOnly, a fully homomorphic encryption protocol. TollsOnly is expected to be a post-quantum privacy preservation scheme. It enables users to share specific data with smart cities via blockchain technology. TollsOnly protects driver privacy in compliance with the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act. 

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